Results 81 to 90 of about 34,284 (266)

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Hyperspherical Variational Auto-Encoders

, 2018
Code at http://github.com/nicola-decao/s-vae-tf and https://github.com/nicola-decao/s-vae-pytorch, Blogpost: https://nicola-decao.github.io/s ...
Davidson, T.R., Falorsi, L., De Cao, N., Kipf, T., Tomczak, J.M.   +4 more
openaire   +3 more sources

Surface‐Capped Protein Nanoparticles for Nonviral Gene Delivery

Advanced Materials, EarlyView.
ABSTRACT Developing simple, safe, and efficient nonviral delivery systems remains a significant challenge in bioengineering. Nanoparticles offer promising gene delivery capabilities with reduced toxicity; however, long‐standing challenges related to effective plasmid encapsulation and delivery exist.
Fjorela Xhyliu, Yao Yao, Yeongun Ko, Grant Grasman, Jeffery E. Raymond, Albert Chang, Yuxuan Deng, Grant Dominic, Michael Triebwasser, Joerg Lahann   +9 more
wiley   +1 more source

Auto-Encoding Variational Bayes

Cambridge Explorations in Arts and Sciences
This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distributions and large-scale datasets. It has been applied to the mnist dataset and extended to form a Dynamic Bayesian Network (DBN) in the context of time ...
Yankun Chen, Jingxuan Liu, Lingyun Peng, Yiqi Wu, Yige Xu, Zhanhao Zhang   +5 more
openaire   +3 more sources

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

Journal of Low Frequency Noise, Vibration and Active Control
The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms
T Praveen Kumar, R Buvaanesh, M Saimurugan, G Naresh, Solomon Jenoris Muthiya, Murgayya Basavanakattimath   +5 more
doaj   +1 more source

Microfluidic Hydroporation Platform for Effective Allogeneic CAR‐T Cell Production and Subsequent Functional Cytotoxicity Analysis

Advanced Materials Technologies, EarlyView.
The hydroporator platform employs controlled hydrodynamic deformation for efficient mRNA and CRISPR/Cas9 delivery into primary human T cells, enabling allogeneic CAR‐T cell manufacturing. It preserves cell functionality and drives potent gene editing, CAR expression, and tumor cytotoxicity, while feature‐based analysis links these functional outcomes ...
Soohyun Jeon, Sein Kim, Hyun‐Young Kim, Duck Cho, Sungsu Park, Seohyun Kim, Aram J. Chung   +6 more
wiley   +1 more source

Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

Machines
The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective.
Wenyou Du, Jingyi Zhang, Guanglei Meng, Haoran Zhang   +3 more
doaj   +1 more source

Information Potential Auto-Encoders

, 2017
In this paper, we suggest a framework to make use of mutual information as a regularization criterion to train Auto-Encoders (AEs). In the proposed framework, AEs are regularized by minimization of the mutual information between input and encoding variables of AEs during the training phase. In order to estimate the entropy of the encoding variables and
Zhang, Yan, Ozay, Mete, Sun, Zhun, Okatani, Takayuki   +3 more
openaire   +2 more sources

Engineered Microfluidic Organoid Systems: New Paradigms for Menopause Mechanism Research and Personalized Medicine

Advanced Materials Technologies, EarlyView.
This review explores the integration of microfluidic technology with organoid systems as an innovative platform for studying menopausea complex multi‐organ condition. By enabling precise simulation of inter‐organ communication and hormone responses, microfluidic organoids offer a physiologically relevant model for investigating menopausal syndrome and ...
Qianyi Zhang, He Huang, Hongliang Dong, Qiang Ma, Hongyan Jin   +4 more
wiley   +1 more source

A deep auto-encoder model for gene expression prediction

BMC Genomics, 2017
Background Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we
Rui Xie, Jia Wen, Andrew Quitadamo, Jianlin Cheng, Xinghua Shi   +4 more
doaj   +1 more source